code: def plot_classification_metrics(estimator, X_val, y_val):
      """Show classification metrics on a validation set.
  
      Parameters
      ----------
      estimator : sklearn or dabl estimator
          Trained estimator
      X_val : DataFrame
          Validation set features
      y_val : Series
          Validation set target.
      """
      y_pred = estimator.predict(X_val)
      print(classification_report(y_val, y_pred))
      print(confusion_matrix(y_val, y_pred))
      try:
          from sklearn.metrics import plot_roc_curve
  
          if len(estimator.classes_) == 2:
              plot_roc_curve(estimator, X_val, y_val)
          elif len(estimator.classes_) > 2:
              plot_multiclass_roc_curve(estimator, X_val, y_val)
      except ImportError:
          warn("Can't plot roc curve, install sklearn 0.22-dev")
  
file_path: dabl/dabl/explain.py
imports: import numpy
  import pandas
  from warnings import warn
  import matplotlib.pyplot
  from sklearn.tree import DecisionTreeClassifier, plot_tree
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.metrics import classification_report, confusion_matrix
  from sklearn.inspection import PartialDependenceDisplay
  from sklearn.feature_selection import f_classif
  from sklearn.impute import SimpleImputer
  from .models import SimpleClassifier, SimpleRegressor, AnyClassifier
  from .utils import _validate_Xyt
  from .plot.utils import plot_coefficients, plot_multiclass_roc_curve, find_pretty_grid, _make_subplots, _find_inliers, _get_scatter_alpha, _get_scatter_size
label_desc: Use image node_id 1 for calling a global function with example usage: plot_classification_metrics(estimator, X_val, y_val) without return types